Emission Tomography (ET), for example, Positron Emission Tomography (PET) imaging, Single Photon Emission Computed Tomography (SPECT) imaging, and the like produces images of various biological processes and functions. In PET imaging, a solution including a tracer is injected into a subject (e.g., a human patient) to be scanned. The tracer is a pharmaceutical compound including a radioisotope with a relatively short half-life, for example, 18F-Fluoro-2-Deoxyglucose (FDG), which is a type of sugar that includes radioactive fluorine. The tracer may be adapted such that it is attracted to sites such as lesions within the subject, where specific biological or biochemical processes occur. Typically, the tracer moves towards and is taken up in one or more organs of the subject in which these biological and biochemical processes occur. For example, cancer cells may metabolize the tracer, allowing a PET scanner to create an image illuminating the cancerous region. Once the radioisotope decays, it emits a positron, which travels a short distance before annihilating with an electron. The short distance, also referred to as the positron range, is typically of the order of 1 mm for FDG in common subjects. The annihilation produces two high-energy photons propagating in substantially opposite directions.
PET imaging uses a photon detector array arranged around a scanning area, usually in a ring-shaped pattern, in which the subject or at least the part of interest of the subject is arranged. When the detector array detects the two photons within a short timing window, a so-called “coincidence” is recorded. A line connecting the two detectors that received the photons is called the Line Of Response (LOR). The reconstruction of a PET image is based on the premise that the decayed radioisotope is located somewhere on the LOR. The relatively short positron range may be neglected or may be compensated in the reconstruction. Each coincidence may be recorded in a list by three entries, wherein two entries represent the two detectors and one entry represents the time of detection. The coincidences in the list may be grouped in one or more sinograms. A sinogram is typically processed using image reconstruction algorithms to obtain volumetric images of the subject. However, PET imaging and SPECT imaging, typically fail to provide structural details of the subject as accurately as other types of scanners, for example, a Computed Tomography (CT) scanner, a Magnetic Resonance Imaging scanner, and the like.
A PET/CT scanner includes a CT scanner and a PET scanner installed around a single patient bore. A PET/CT scanner creates a fused volumetric image including a PET image spatially registered to a CT image. Similarly, a SPECT/CT scanner includes a SPECT scanner and a CT scanner installed around a single patient bore and creates a fused volumetric image including a SPECT image spatially registered to a CT image. PET/CT scanners and SPECT/CT scanners provide the advantage that the functional and biological features shown by the PET image or the SPECT image may be precisely located with respect to the structural details provided by the CT image.
In a typical ET/CT scan, the patient first undergoes a CT scan, and then the patient undergoes a ET scan before exiting the scanner. After the CT and ET data have been acquired, the ET/CT scanner processes the data and generates the fused ET/CT image. In order to generate quantitatively accurate ET images, the ET data needs to be corrected for several patient and system related factors. The most important patient related factor is the attenuation of the high-energy photons from an annihilation event, as they traverse through the patient body. In order to account for this effect, the CT data is used to compute the line attenuation co-efficients, a material dependent property, along the travel path of the high-energy photons. These attenuation co-efficients are used during the tomographic reconstruction of ET data.
Patient motion (e.g., motion due to respiration) is another factor in degrading the quantitative integrity of ET images. Respiratory motion may result in artifacts and/or contrast dilution of lesions due to motion blurring. Respiratory-gated acquisition of ET data may reduce motion blur. In a respiratory-gated acquisition, the ET data is partitioned during each respiratory cycle to produce independent ET images for each partition or gate. In order to generate quantitatively accurate ET images for each partition or gate, a corresponding CT image needs to be generated for attenuation correction.
Typically, the partitioning of ET and CT data is performed based on a respiratory curve produced by external motion monitoring devices, for example, camera based systems, pressure sensitive belts, pyrometers, and the like. However, such methods are disadvantageous since the external motion monitoring devices are expensive, complicate workflow and are cumbersome to use in a clinical setting. To overcome this, data-driven techniques have been developed to extract patient motion information from the ET and CT data. Such data driven techniques are disadvantageous as they cannot be applied in real-time and have to be applied retrospectively upon the completion of data acquisition.
An important consideration during the acquisition and partitioning of the CT data is the acquisition time of the CT data. The CT acquisition needs to be long enough to capture at least an entire respiratory cycle. However, since x-rays are harmful to the subject, the CT data acquisition should not be much longer than a respiratory cycle. When external motion monitoring device are used, an administrator of the ET/CT system analyzes a respiratory curve and prescribes an acquisition time for acquiring CT data. In addition to being cumbersome and expensive, methods based on external motion monitoring device may lead to a non-optimal acquisition time, since the prescribed acquisition time is subjective and dependent on the experience and perception of the administrator. Data-driven techniques based on CT data are also disadvantageous since the respiratory curve is not available a priori, in order to set an optimal acquisition time.
Thus, there is a need for an enhanced system and method for CT data acquisition.